Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective.

Treatment failure for the lethal brain tumor glioblastoma (GBM) is attributed to intratumoral heterogeneity and tumor evolution. We utilized 3D neuronavigation during surgical resection to acquire samples representing the whole tumor mapped by 3D spatial coordinates. Integrative tissue and single-cell analysis revealed sources of genomic, epigenomic, and microenvironmental intratumoral heterogeneity and their spatial patterning. By distinguishing tumor-wide molecular features from those with regional specificity, we inferred GBM evolutionary trajectories from neurodevelopmental lineage origins and initiating events such as chromothripsis to emergence of genetic subclones and spatially restricted activation of differential tumor and microenvironmental programs in the core, periphery, and contrast-enhancing regions. Our work depicts GBM evolution and heterogeneity from a 3D whole-tumor perspective, highlights potential therapeutic targets that might circumvent heterogeneity-related failures, and establishes an interactive platform enabling 360° visualization and analysis of 3D spatial patterns for user-selected genes, programs, and other features across whole GBM tumors.

Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Stromal Microenvironment Shapes the Intratumoral Architecture of Pancreatic Cancer.

Single-cell technologies have described heterogeneity across tissues, but the spatial distribution and forces that drive single-cell phenotypes have not been well defined. Combining single-cell RNA and protein analytics in studying the role of stromal cancer-associated fibroblasts (CAFs) in modulating heterogeneity in pancreatic cancer (pancreatic ductal adenocarcinoma [PDAC]) model systems, we have identified significant single-cell population shifts toward invasive epithelial-to-mesenchymal transition (EMT) and proliferative (PRO) phenotypes linked with mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription 3 (STAT3) signaling. Using high-content digital imaging of RNA in situ hybridization in 195 PDAC tumors, we quantified these EMT and PRO subpopulations in 319,626 individual cancer cells that can be classified within the context of distinct tumor gland "units." Tumor gland typing provided an additional layer of intratumoral heterogeneity that was associated with differences in stromal abundance and clinical outcomes. This demonstrates the impact of the stroma in shaping tumor architecture by altering inherent patterns of tumor glands in human PDAC.

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics.

Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

Early Injury Landscape in Vein Harvest by Single-Cell and Spatial Transcriptomics.

BACKGROUND: Vein graft failure following cardiovascular bypass surgery results in significant patient morbidity and cost to the healthcare system. Vein graft injury can occur during autogenous vein harvest and preparation, as well as after implantation into the arterial system, leading to the development of intimal hyperplasia, vein graft stenosis, and, ultimately, bypass graft failure. Although previous studies have identified maladaptive pathways that occur shortly after implantation, the specific signaling pathways that occur during vein graft preparation are not well defined and may result in a cumulative impact on vein graft failure. We, therefore, aimed to elucidate the response of the vein conduit wall during harvest and following implantation, probing the key maladaptive pathways driving graft failure with the overarching goal of identifying therapeutic targets for biologic intervention to minimize these natural responses to surgical vein graft injury. METHODS: Employing a novel approach to investigating vascular pathologies, we harnessed both single-nuclei RNA-sequencing and spatial transcriptomics analyses to profile the genomic effects of vein grafts after harvest and distension, then compared these findings to vein grafts obtained 24 hours after carotid-carotid vein bypass implantation in a canine model (n=4). RESULTS: Spatial transcriptomic analysis of canine cephalic vein after initial conduit harvest and distention revealed significant enrichment of pathways (P<0.05) involved in the activation of endothelial cells (ECs), fibroblasts, and vascular smooth muscle cells, namely pathways responsible for cellular proliferation and migration and platelet activation across the intimal and medial layers, cytokine signaling within the adventitial layer, and ECM (extracellular matrix) remodeling throughout the vein wall. Subsequent single-nuclei RNA-sequencing analysis supported these findings and further unveiled distinct EC and fibroblast subpopulations with significant upregulation (P<0.05) of markers related to endothelial injury response and cellular activation of ECs, fibroblasts, and vascular smooth muscle cells. Similarly, in vein grafts obtained 24 hours after arterial bypass, there was an increase in myeloid cell, protomyofibroblast, injury response EC, and mesenchymal-transitioning EC subpopulations with a concomitant decrease in homeostatic ECs and fibroblasts. Among these markers were genes previously implicated in vein graft injury, including VCAN, FBN1, and VEGFC, in addition to novel genes of interest, such as GLIS3 and EPHA3. These genes were further noted to be driving the expression of genes implicated in vascular remodeling and graft failure, such as IL-6, TGFBR1, SMAD4, and ADAMTS9. By integrating the spatial transcriptomics and single-nuclei RNA-sequencing data sets, we highlighted the spatial architecture of the vein graft following distension, wherein activated and mesenchymal-transitioning ECs, myeloid cells, and fibroblasts were notably enriched in the intima and media of distended veins. Finally, intercellular communication network analysis unveiled the critical roles of activated ECs, mesenchymal-transitioning ECs, protomyofibroblasts, and vascular smooth muscle cells in upregulating signaling pathways associated with cellular proliferation (MDK [midkine], PDGF [platelet-derived growth factor], VEGF [vascular endothelial growth factor]), transdifferentiation (Notch), migration (ephrin, semaphorin), ECM remodeling (collagen, laminin, fibronectin), and inflammation (thrombospondin), following distension. CONCLUSIONS: Vein conduit harvest and distension elicit a prompt genomic response facilitated by distinct cellular subpopulations heterogeneously distributed throughout the vein wall. This response was found to be further exacerbated following vein graft implantation, resulting in a cascade of maladaptive gene regulatory networks. Together, these results suggest that distension initiates the upregulation of pathological pathways that may ultimately contribute to bypass graft failure and presents potential early targets warranting investigation for targeted therapies. This work highlights the first applications of single-nuclei and spatial transcriptomic analyses to investigate venous pathologies, underscoring the utility of these methodologies and providing a foundation for future investigations.

Spatial effects of infiltrating T cells on neighbouring cancer cells and prognosis in stage III CRC patients.

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Multiplex imaging reveals spatially resolved DNA-damage response neighborhoods in TP53-mutated myelodysplastic neoplasms.

While increased DNA damage is a well-described feature of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), it is unclear whether all lineages and all regions of the marrow are homogeneously affected. In this study, we performed immunohistochemistry on formalin-fixed, paraffin-embedded whole-section bone marrow biopsies using a well-established antibody to detect pH2A.X (phosphorylated histone variant H2A.X) that recognizes DNA double-strand breaks. Focusing on TP53-mutated and complex karyotype MDS/AML, we find a greater pH2A.X+ DNA damage burden compared to TP53 wild-type neoplastic cases and non-neoplastic controls. To understand how double-strand breaks vary between lineages and spatially in TP53-mutated specimens, we applied a low-multiplex immunofluorescence staining and spatial analysis protocol to visualize pH2A.X+ cells with p53 protein staining and lineage markers. pH2A.X marked predominantly mid- to late-stage erythroids, whereas early erythroids and CD34+ blasts were relatively spared. In a prototypical example, these pH2A.X+ erythroids were organized locally as distinct colonies, and each colony displayed pH2A.X+ puncta at a synchronous level. This highly coordinated immunophenotypic expression was also seen for p53 protein staining and among presumed early myeloid colonies. Neighborhood clustering analysis showed distinct marrow regions differentially enriched in pH2A.X+/p53+ erythroid or myeloid colonies, indicating spatial heterogeneity of DNA-damage response and p53 protein expression. The lineage and architectural context within which DNA damage phenotype and oncogenic protein are expressed is relevant to current therapeutic developments that leverage macrophage phagocytosis to remove leukemic cells in part due to irreparable DNA damage. © 2024 The Pathological Society of Great Britain and Ireland.

Spatial scale of tuberculosis transmission in Lima, Peru.

Spatially targeted interventions may be effective alternatives to individual or population-based prevention strategies against tuberculosis (TB). However, their efficacy may depend on the mechanisms that lead to geographically constrained hotspots. Local TB incidence may reflect high levels of local transmission; conversely, they may point to frequent travel of community members to high-risk areas. We used whole-genome sequencing to explore patterns of TB incidence and transmission in Lima, Peru. Between 2009 and 2012, we recruited incident pulmonary TB patients and their household contacts, whom we followed for the occurrence of TB disease. We used whole-genome sequences of 2,712 Mycobacterial tuberculosis isolates from 2,440 patients to estimate pariwise genomic distances and compared these to the spatial distance between patients' residences. Genomic distances increased rapidly as spatial distances increased and remained high beyond 2 km of separation. Next, we divided the study catchment area into 1 × 1 km grid-cell surface units and used household spatial coordinates to locate each TB patient to a specific cell. We estimated cell-specific transmission by calculating the proportion of patients in each cell with a pairwise genomic distance of 10 or fewer single-nucleotide polymorphisms. We found that cell-specific TB incidence and local transmission varied widely but that cell-specific TB incidence did not correlate closely with our estimates of local transmission (Cohen's k = 0.27). These findings indicate that an understanding of the spatial heterogeneity in the relative proportion of TB due to local transmission may help guide the implementation of spatially targeted interventions.

Neighborhood-level variability in asthma-related emergency department visits in Central Texas.

BACKGROUND: The extent to which incidence rates of asthma-related emergency department (ED) visits vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood. OBJECTIVE: We aimed to describe the census tract-level spatial distribution of asthma-related ED visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates. METHODS: Conditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across census tracts in Travis County, Texas, and assess the contribution of census tract characteristics to their distribution. RESULTS: There were distinct patterns in ED visit incidence rates at the census tract scale. These patterns were largely unexplained by socioeconomic or selected built environment neighborhood characteristics. However, racial and ethnic composition explained 33% of the variability of ED visit incidence rates across census tracts. The census tract predictors of ED visit incidence rates differed by racial and ethnic group. CONCLUSIONS: Variability in asthma ED visit incidence rates are apparent at smaller spatial scales. Most of the variability in census tract-level asthma ED visit rates in Central Texas is not explained by racial and ethnic composition or other neighborhood characteristics.

Spatial Analysis of Drug-Susceptible and Multidrug-Resistant Cases of Tuberculosis, Ho Chi Minh City, Vietnam, 2020-2023.

We characterized the spatial distribution of drug-susceptible (DS) and multidrug-resistant (MDR) tuberculosis (TB) cases in Ho Chi Minh City, Vietnam, a major metropolis in southeastern Asia, and explored demographic and socioeconomic factors associated with local TB burden. Hot spots of DS and MDR TB incidence were observed in the central parts of Ho Chi Minh City, and substantial heterogeneity was observed across wards. Positive spatial autocorrelation was observed for both DS TB and MDR TB. Ward-level TB incidence was associated with HIV prevalence and the male proportion of the population. No ward-level demographic and socioeconomic indicators were associated with MDR TB case count relative to total TB case count. Our findings might inform spatially targeted TB control strategies and provide insights for generating hypotheses about the nature of the relationship between DS and MDR TB in Ho Chi Minh City and the wider southeastern region of Asia.

Associations between flood risk and US Census tract-level health outcomes.

Human-induced climate change has led to more frequent and severe flooding around the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation data by census tract and used principal component analysis to derive a set of 5 interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on these data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We show that 3 flood risk principal components had small but significant associations with each of the health outcomes across the different stratifications of social vulnerability. Our analysis gives, to our knowledge, the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards. This article is part of a Special Collection on Environmental Epidemiology.

Characterization of indigenous populations of cannabis in Iran: a morphological and phenological study.

BACKGROUND: Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars. RESULTS: Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g- 1.day- 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants. CONCLUSIONS: Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes.

Quantitative Multiplexed Analysis of Indoleamine 2,3-Dioxygenase (IDO) and Arginase-1 (ARG1) Expression and Myeloid Cell Infiltration in Colorectal Cancer.

Indoleamine 2,3-dioxygenase (IDO) and arginase-1 (ARG1) are amino acid-metabolizing enzymes, frequently highly expressed in cancer. Their expression may deplete essential amino acids, lead to immunosuppression, and promote cancer growth. Still, their expression patterns, prognostic significance, and spatial localization in the colorectal cancer microenvironment are incompletely understood. Using a custom 10-plex immunohistochemistry assay and supervised machine learning-based digital image analysis, we characterized IDO and ARG1 expression in monocytic cells, granulocytes, mast cells, and tumor cells in 833 colorectal cancer patients. We evaluated the prognostic value and spatial arrangement of IDO- and ARG1-expressing myeloid and tumor cells. IDO was mainly expressed not only by monocytic cells but also by some tumor cells, whereas ARG1 was predominantly expressed by granulocytes. Higher density of IDO+ monocytic cells was an independent prognostic factor for improved cancer-specific survival both in the tumor center (Ptrend = .0002; hazard ratio [HR] for the highest ordinal category Q4 [vs Q1], 0.51; 95% CI, 0.33-0.79) and the invasive margin (Ptrend = .0015). Higher density of granulocytes was associated with prolonged cancer-specific survival in univariable models, and higher FCGR3+ARG1+ neutrophil density in the tumor center also in multivariable analysis (Ptrend = .0020). Granulocytes were, on average, located closer to tumor cells than monocytic cells. Furthermore, IDO+ monocytic cells and ARG1- granulocytes were closer than IDO- monocytic cells and ARG1+ granulocytes, respectively. The mRNA expression of the IDO1 gene was assessed in myeloid and tumor cells using publicly available single-cell RNA sequencing data for 62 colorectal cancers. IDO1 was mainly expressed in monocytes and dendritic cells, and high IDO1 activity in monocytes was associated with enriched immunostimulatory pathways. Our findings provided in-depth information about the infiltration patterns and prognostic value of cells expressing IDO and/or ARG1 in the colorectal cancer microenvironment, highlighting the significance of host immune response in tumor progression.

Neighboring macrophage-induced alteration in the phenotype of colorectal cancer cells in the tumor budding area.

BACKGROUND: A higher number of tumor buds in the invasive front of colorectal cancer (CRC) specimens has been shown to contribute to a poor prognosis in CRC patients. Because macrophages (Mφs) have been demonstrated to alter the phenotype of cancer cells, we hypothesized that the phenotype of CRC cells in the tumor budding (TB) area might be changed by the interaction between CRC cells and Mφs. METHODS: We assessed the expression of topoisomerase 1 in CRC cells to estimate the acquisition of chemoresistance in CRC. To demonstrate the tumor-stromal interaction between CRC cells and Mφs, we assessed two histological findings, the number of Mφs per single CRC cell and the proximity between CRC cells and Mφs by histological spatial analysis using HALO software. RESULTS: The expression levels of topoisomerase 1 in CRC cells were decreased in deeper areas, especially in the TB area, compared to the surface area. Our histological spatial analysis revealed that 2.6 Mφs located within 60 µm of a single CRC cell were required to alter the phenotype of the CRC cell. Double-immunofluorescence staining revealed that higher Mφs were positive for interleukin-6 (IL-6) in the TB area and that AE1/AE3-positive CRC cells were also positive for phospho-STAT3 (pSTAT3) in the TB area; thus, the IL-6 receptor (IL-6R)/STAT3 signaling pathway in CRC cells was upregulated by IL-6 derived from neighboring Mφs. CONCLUSION: IL-6 secreted from the neighboring Mφs would alter the phenotype of CRC cells via IL-6R/STAT3 signaling pathway.

Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey.

BACKGROUND: Regular Low-Dose Computed Tomography (LDCT) for lung cancer high-risk population has been proved to improve health outcomes and relieve disease burden efficiently for both individual and society. With geographical impedance becoming the major barrier preventing patients from getting timely healthcare service, this study incorporated health seeking behavior in estimating spatial accessibility of relative scarce LDCT resource in China, thus to provide real-world evidence for future government investment and policy making. METHODS: Taking Sichuan Province in southwest China as the study area, a cross-sectional survey was first carried out to collect actual practice and preferences for seeking LDCT services. Using Computed Tomography (CT) registration data reported by owner institutions representing LDCT services capacity, and grided town-level high-risk population as demand, the Nearest Neighbor Method was then utilized to calculate spatial accessibility of LDCT services. RESULTS: A total of 2,529 valid questionnaires were collected, with only 34.72% of the high-risk populations (746 individuals) followed the recommended annual screening. Participants preferred to travel to municipal-level and above institutions within 60 min for LDCT services. Currently, every thousand high-risk populations own 0.0845 CT scanners in Sichuan Province, with 96.95% able to access LDCT within 60 min and over half within 15 min. Urban areas generally showed better accessibility than rural areas, and the more developed eastern regions were better than the western regions with ethnic minority clusters. CONCLUSIONS: Spatial access to LDCT services is generally convenient in Sichuan Province, but disparity exists between different regions and population groups. Improving LDCT capacity in county-level hospitals as well as promoting health education and policy guidance to the public can optimize efficiency of existing CT resources. Implementing mobile CT services and improving rural public transportation may alleviate emerging disparities in accessing early lung cancer detection.

Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis.

OBJECTIVES: Although the link between poverty and tuberculosis (TB) is widely recognised, limited studies have investigated the association between neighbourhood factors and TB incidence. Since the factors influencing different episodes of TB might be different, this study focused on the first episode of TB disease (first-episode TB). METHODS: All first episodes in previously linked and geocoded TB notification data from 2007 to 2015 in Cape Town, South Africa, were aggregated at the neighbourhood level and merged with the 2011 census data. We conducted an ecological study to assess the association between neighbourhood incidence of first-episode TB and neighbourhood factors (total TB burden [all episodes] in the previous year, socioeconomic index, mean household size, mean age, and percentage males) using a negative binomial regression. We also examined the presence of hotspots in neighbourhood TB incidence with the Global Moran's I statistic and assessed spatial dependency in the association between neighbourhood factors and TB incidence using a spatial lag model. RESULTS: The study included 684 neighbourhoods with a median first-episode TB incidence rate of 114 (IQR: 0-345) per 100,000 people. We found lower neighbourhood socioeconomic index (SEI), higher neighbourhood total TB burden, lower neighbourhood mean household size, and lower neighbourhood mean age were associated with increased neighbourhood first-episode TB incidence. Our findings revealed a hotspot of first-episode TB incidence in Cape Town and evidence of spatial dependency in the association between neighbourhood factors and TB incidence. CONCLUSION: Neighbourhood TB burden and SEI were associated with first-episode TB incidence, and there was spatial dependency in this association. Our findings can inform targeted interventions to reduce TB in high-risk neighbourhoods, thereby reducing health disparities and promoting health equity.

Geospatial analysis and prevalence of Schistosoma mansoni and soil-transmitted helminth infections in an endemic area in Eastern Brazilian Amazon.

OBJECTIVES: This study evaluated the occurrence of Schistosoma mansoni and soil-transmitted helminths in an endemic area in the Eastern Brazilian Amazon, analysing prevalence and spatial distribution. METHODS: The study was conducted in four localities of Primavera Municipality, in Pará state. Data was obtained from the Decit 40/2012 project and the participants were divided into five age range categories for evaluation: children, adolescents, young adults, adults and elderly individuals. For the diagnostic tests, Kato-Katz slides were prepared to detect S. mansoni and soil-transmitted helminths eggs. The spatial distribution map and the Kernel Density Estimation were performed to assess the presence and location of infections. RESULTS: Stool samples revealed the presence of hookworms, S. mansoni, Ascaris lumbricoides and Trichuris trichiura eggs. Mono-, bi- and poly-parasitic infections were observed, with a significant prevalence of hookworm monoparasitism. CONCLUSIONS: The high frequency of children infected with soil-transmitted helminths confirms their significance as an ongoing public health problem in the poorest municipalities of Brazil. The Geographic Information System plays a crucial role in environmental surveillance and in the control of epidemics and endemic diseases, enabling accurate assessment and informed decision-making for their control.

Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China.

BACKGROUND: In China, the problem of HIV infection among the older people has become increasingly prominent. This study aimed to analyze the pattern and influencing factors of HIV transmission based on a genomic and spatial epidemiological analysis among this population. METHODS: A total of 432 older people who were aged ≥ 50 years, newly diagnosed with HIV-1 between January 2018 and December 2021 and without a history of ART were enrolled. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. The molecular transmission network was constructed using HIV-TRACE and the spatial distribution analyses were performed in ArcGIS. The multivariate logistic regression analysis was performed to analyze the factors associated with clustering. RESULTS: A total of 382 sequences were successfully sequenced, of which CRF07_BC (52.3%), CRF01_AE (32.5%), and CRF08_BC (6.8%) were the main HIV-1 strains. A total of 176 sequences entered the molecular network, with a clustering rate of 46.1%. Impressively, the clustering rate among older people infected through commercial heterosexual contact was as high as 61.7% and three female sex workers (FSWs) were observed in the network. The individuals who were aged ≥ 60 years and transmitted the virus by commercial heterosexual contact had a higher clustering rate, while those who were retirees or engaged other occupations and with higher education degree were less likely to cluster. There was a positive spatial correlation of clustering rate (Global Moran I = 0.206, P < 0.001) at the town level and the highly aggregated regions were mainly distributed in rural area. We determined three large clusters which mainly spread in the intra-region of certain towns in rural areas. Notably, 54.5% of cases in large clusters were transmitted through commercial heterosexual contact. CONCLUSIONS: Our joint analysis of molecular and spatial epidemiology effectively revealed the spatial aggregation of HIV transmission and highlighted that towns of high aggregation were mainly located in rural area. Also, we found vital role of commercial heterosexual contact in HIV transmission among older people. Therefore, health resources should be directed towards highly aggregated rural areas and prevention strategy should take critical persons as entry points.